An electronic device always comprises at least one electronic component (e.g. an antenna, a transducer, a sensor, an active and/or passive filter, an integrated circuit, a power supply/battery) and a plurality of signal paths through which various signals (e.g. input, feedback, control, and output signals) propagate. A signal path may in turn be a signal chain, that is, a series of signal-conditioning electronic components that receive input (data acquired from sampling either real-time phenomena or from stored data) in tandem, with the output of one portion of the chain supplying input to the next.
Signals of interest in various signal paths of an electronic device (such as, for example, a communication or data acquisition and processing device, a biomedical device, or a computer) are affected by various interferences (noise) from natural and man-made sources. Be it a signal from a sensor, or a signal from a transmitter in a communication chain, the amount of noise affecting the signal may be reduced to improve the signal quality and/or other properties of the device (e.g. reduce its size and/or power consumption, the bill of materials, and/or the cost of the components).
For example, the demand for wireless Internet data is exponentially increasing, and the interference in wireless receivers “is the key bottleneck preventing service providers from meeting this demand” (see Chopra [7, p. 21]). This interference comes from various sources including, but not limited to, the circuit noise and the interference from extraneous sources, such as conductive electromagnetic interference (EMI-conductive) and radio frequency interference (RFI), intelligent (co-channel, adjacent-channel interference (ACI)) as well as non-intelligent (commercial electronic devices, powerlines, and platform (clocks, amplifiers, co-located transceivers)) sources, and self-interference (multipath). Such technogenic noise is typically non-Gaussian, and often impulsive (Slattery and Skinner [31], Chopra [7]).
Electrical noise is transmitted into a system through the galvanic (direct electrical contact), electrostatic coupling, electromagnetic induction, or RFI ways. An inappropriate electronic design or layout, or insufficient radio frequency (RF) shielding may drastically reduce system performance and lead to “unexplainable” or “random” system failures or an overall reduction in system performance. Design, layout, and shielding considerations may significantly increase the size, weight, bill of materials, and the cost of an electronic device or system.
A particular example of impulsive interference is electromagnetic interference (EMI), also called radio frequency interference (RFI). It is a widely recognized cause of reception problems in communications and navigation devices. EMI is a disturbance that affects an electrical circuit due to either conduction or radiation emitted from a source internal or external to the device. EMI may interrupt, obstruct, or otherwise degrade the effective performance of the device, and limit its link budget. The detrimental effects of EMI are broadly acknowledged in the industry and include: (i) reduced signal quality to the point of reception failure, (ii) increased bit errors which degrade the system and results in lower data rates and decreased reach, and (iii) increased power output of the transmitter, which increases its interference with nearby receivers and reduces the battery life of a device.
A major and rapidly growing source of EMI in communication and navigation receivers is other transmitters that are relatively close in frequency and/or distance to the receivers. Multiple transmitters and receivers are increasingly combined in single devices, which produces mutual interference. A typical example is a smartphone equipped with cellular, WiFi, Bluetooth, and GPS receivers, or a mobile WiFi hotspot containing an HSDPA and/or LTE receiver and a WiFi transmitter operating concurrently in close physical proximity. Other typical sources of strong EMI are on-board digital circuits, clocks, buses, and switching power supplies. This physical proximity, combined with a wide range of possible transmit and receive powers, creates a variety of challenging interference scenarios. Existing empirical evidence (Slattery and Skinner [31], Leferink et al. [15], Nikitin et al. [22]) and its theoretical support (Nikitin [23, 24]) show that such interference often manifests itself as impulsive noise, which in some instances may dominate over the thermal noise (Yang and Petropulu [33], Slattery and Skinner [31], Nikitin et al. [22]).
A particular source of impulsive noise in digital communication systems is interchannel interference (Nikitin [23, 24], Nikitin et al. [22]). For example, a strong close transmitter (e.g. WiFi) may noticeably interfere with a receiver of a weak signal (e.g. GPS) even when the separation of their frequency bands exceeds the respective nominal bandwidths of the channels by orders of magnitude. When time domain observations of such far-out-of-band interference are made at the receiver frequency, in a relatively wide bandwidth to avoid excessive broadening of the transients, this interference is likely to appear impulsive.
The amount of the interchannel out-of-band (OOB) interference depends on the strength of the antenna coupling (Nikitin et al. [22]). This coupling may be changed by the shape and the orientation of the antennas, shielding, and the distance between the antennas. Increasing the distance between the antennas generally contributes to the overall size of the device (e.g. smartphone), while shielding increases its weight, bill of materials, and its cost.
The OOB emissions may be partially mitigated by additional filtering. For example, one may apply additional high-order lowpass filtering to the modulating signal, or bandpass filtering to the modulated carrier, under the constraint that the bandwidth of those additional filters must be sufficiently large in comparison with the bandwidth of the pulse shaping filter in the modulator in order to not significantly affect the designed signal (Nikitin [23, 24]). These additional filters increase the circuit complexity, component count, size and cost, and decrease the reliability of the device.
The non-idealities in hardware implementation of designed modulation schemes such as the non-smooth behavior of the modulator around zero exacerbate the OOB emissions (Nikitin [23, 24], Nikitin et al. [22]). Thus, in order to keep these emissions at a low level, expensive high-quality components such as integrated circuit (IC) modulators and power amplifiers may be used, which increases the complexity and the cost of the components. The OOB emissions are also exacerbated by the coupling of other interfering signals from the adjacent circuitry (Nikitin et al. [22]), which imposes additional limitations on the layout, shielding, and the overall size and cost of the device, and limits the amount of space left for other components, e.g. a battery.
The impulsive noise problem also arises when devices based on the Ultra-wideband (UWB) technology interfere with narrowband communication systems such as WLAN (Mallipeddy and Kshetrimayum [16]) or CDMA-based cellular systems (Fischer [10]). A UWB device is seen by a narrowband receiver as a source of impulsive noise, which degrades the performance of the receiver and increases its power consumption (Fischer [10]).
As an example for wired communication systems, a major impairment for Digital Subscriber Line (DSL) technologies is impulse noise in the telephone lines (Dragomir et al. [9]). This noise limits the performance of a DSL system, and increases its cost and power consumption through the necessity to deploy various nonlinear impulsive noise reduction techniques.
As yet another example, capacitive touchscreens in modern smartphones are ubiquitous but prone to false and erratic response due to noise from the product in which they reside. Noise comes from both the internal DC/DC-converter subsystem and the display drivers. One of the steady current trends in the telecommunications industry is the push toward thinner phones with multi-touch displays. Achieving this goal means direct lamination of capacitive-touch sensors to the display, moving the sensor inside the display, and overcoming many other challenges with antennas and ground loading. It is no longer acceptable to just use a shield layer in the sensor structure to block display noise, as it adds too much cost and thickness. Also, charger noise physically couples into the sensor through the battery charger during the presence of touch. Its effects include degraded accuracy or linearity of touch, false or phantom touches, or even an unresponsive or erratic touchscreen (Carey [6]).
Other systems impeded by the impulsive noise and artifacts are various sensor systems, including active radar and all coherent imaging systems such as synthetic aperture radar (SAR) [30]. A common example is various medical imaging systems such as ultrasonic, which are generally affected by multiplicative shot (or speckle) noise. Typically, various methods of reduction of the speckle noise involve non-real-time adaptive and non-adaptive speckle filtering of the acquired images, or multi-look processing. In order to effectively filter the speckle noise, the imaging data bandwidth needs to be greatly increased. This leads to a “too much data” problem and to a dramatic increase in the computational load (e.g. increase in memory and DSP requirements).
Since the introduction of the micromachining process, wherein mechanical structures are etched from blocks of silicon, a number of microelectromechanical systems (MEMS) have been produced. This size reduction is attractive for many applications but, since the ratio of mechanical to thermal energy diminishes as the device mass is reduced, MEMS are susceptible to both internal and external (for example, acoustic) limiting noises, especially in harsh environments, which may often be non-Gaussian and impulsive (see Gabrielson [11], Mohd-Yasin et al. [17], for example).
Advances in digital VLSI technologies lead to wider use of the delta-sigma (ΔΣ) modulation-based analog-to-digital converters (ADCs) as a cost effective alternative for high resolution (greater than 12 bits) converters, which can be ultimately integrated on digital signal processor ICs. However, due to high nonlinearity of the delta-sigma modulation, ΔΣ converters are highly susceptible to misbehavior when their input contains high-amplitude transients (impulse noise) (Ardalan and Paulos [3], Janssen and van Roermund [14]), which decreases the system performance. When such transients are present, larger size and more expensive converters may need to be used, increasing the overall size and cost of a device and its power consumption.
In audio applications, impulse (acoustic) noise includes unwanted, almost instantaneous (thus impulse-like) sharp sounds (like clicks and pops). Noises of this kind are usually caused by electromagnetic interference, scratches on the recording disks, and poor synchronization in digital recording and communication. High levels of such a noise (200+ Decibels) may damage internal organs, while 180 Decibels (e.g. high power gunshots at close distance) are enough to destroy or damage human ears.
An impulse noise filter may be used to enhance the quality of noisy signals, in order to achieve robustness in audio applications, pattern recognition, and adaptive control systems. A classic filter used to remove impulse noise is the median filter, at the expense of signal degradation due to nonlinear distortions introduced by such a filter. Thus it is quite common, in order to get better performing impulse noise filters, to use model-based systems that know the properties of the noise and source signal (in time or frequency), in order to remove only impulse obliterated samples. Such model-based systems are slow (not real-time), and hardware and computationally intensive (e.g. memory and DSP intensive). In addition, digital median filters themselves require memory and are computationally expensive, and thus increase cost, complexity, and power consumption of a system.
Switched-mode power supplies (SMPS) are used as replacements for the linear regulators when higher efficiency, smaller size or lighter weight are required. However, their switching currents cause impulsive noise problems (as both the emitted RFI and the electronic noise at the output terminals) if not carefully suppressed by adequate EMI filtering and RF shielding, which contributes to an increased size, weight, circuit complexity, and cost.
The current trend in SMPSs is toward smaller devices which necessitates higher frequency operation of the SMPS oscillator. Most configurations also allow the clock frequency to vary based on the output load characteristics, making the coupled noise impulsive and somewhat aperiodic. Most of the SMPSs now operate in the range from hundreds of kHz to a few MHz, placing the noise in the same frequency range where the power-supply rejection ratio (PSRR) of analog components reaches a minimum. This necessitates designers to increase the power bus filtering, which adds significant cost.
WirelessHART is a standard that defines a protocol stack that can employ any short range wireless technologies (WLAN, Bluetooth, ZigBee) at its physical layer. Many companies in the health, oil exploration and other sectors have adopted WirelessHART. Its use in electricity supply industry, however, is limited because reliable operation is at risk due to short, but intense, field transients extending into the RF and microwave spectrum during faults and/or switching events [4]. Electrical substations contain transformers, circuit breakers, isolators, cables, voltage regulators, and other equipment for control and protection. Both partial and full discharges may occur within, and across, any degraded insulation forming part of these components of a plant. These discharges generate rapid changes in current and thus lead to the radiation of electromagnetic noise typically consisting of a quasi-random train of short (nanosecond) impulses. Corona discharge is one form of partial discharge, which occurs when the potential gradient in the gas (usually air) around a charged object (which may or may not be a conductor) exceeds the breakdown threshold. Power system switching events and fault transients also give rise to the radiation of unwanted impulsive noise that may interfere with the reliability or performance of wireless receivers generally and wireless sensor networks (WSNs) in particular (Bhatti et al. [4]). Thus there is a need for effective impulsive noise mitigation to enable reliable operation of the devices such as ZigBee receivers in impulsive noise environments.
In any cable or power line communications, impulse noise is known to be the most difficult noise to filter (Guillet et al. [12]). In particular, non periodic asynchronous impulse noise is impossible to predict. To overcome this problem, the signal-to-noise ratio is generally improved by detecting and/or filtering the noise. This leads, however, to heavy detection and computing time in comparison with the disturbance duration, and contributes to the decreased performance and the increased size, weight, circuit complexity, and cost.
Interference mitigation methods may be classified as either static methods (e.g. layout and shielding, spectrum allocation) that avoid interference through device design or network planning, or as active digital methods (e.g. controlling/managing protocols such as multiple access protocols, interference alignment and/or cancellation, or statistical mitigation) that estimate and cancel interference during data transmission (Chopra [7]). All these methods contribute to the decreased performance and the increased power consumption, size, weight, circuit complexity, and cost.
Most state-of-the-art analog mitigation methods of EMI focus on reducing the interference before it reaches the receiver (e.g. through shielding, physical separation to reduce coupling, and other layout techniques), and none of these methods allows effective EMI filtering once it has entered the receiver chain. After the interference has entered the signal path, only computationally and silicon intensive nonlinear, non-real-time digital signal processing solutions are offered.
Since a signal of interest typically occupies a different and/or narrower frequency range than the noise, linear filters are applied to the incoming mixture of the signal and the noise in order to reduce the frequency range of the mixture to that of the signal. This reduces the power of the interference to a fraction of the total, limited to the frequency range of the signal.
However, the noise having the same frequency power spectrum may have various peakedness (for example, as measured by excess kurtosis; see Section 13.2.1 of this disclosure for a discussion of measures of peakedness), and be impulsive or non-impulsive. For example, white shot noise is much more impulsive than white thermal noise, while both have identically flat power spectra. Linear filtering in the frequency domain does not discriminate between impulsive and non-impulsive noise contributions, and does not allow mitigation of the impulsive noise relative to the non-impulsive. In addition, reduction in the bandwidth of an initially impulsive noise by linear filtering typically reduces the peakedness and makes the noise less impulsive (more ‘Gaussian-like’), decreasing the ability to separate the signal from the noise based on the peakedness.
Effective suppression of impulsive interferences in the signal path typically requires nonlinear means, for example, processing based on order statistics. These means may be employed either through digital signal processing, or in the analog signal chain. The nonlinear filters in the analog signal chain may range from simple slew rate limiting filters to more sophisticated analog rank filters described, for example, in U.S. Pat. Nos. 7,133,568 and 7,242,808 (Nikitin and Davidchack [21]), and U.S. Pat. Nos. 7,107,306, 7,418,469, and 7,617,270 (Nikitin [18]).
However, the practical use of nonlinear filters is limited as it typically results in complicated design considerations and in multiple detrimental effects on normal signal flow (signal degradation). These filters may cause various nonlinear distortions and excessive attenuation of the signal, and their effect on the useful signal components is typically unpredictable and depends on the type and magnitude of the interfering signal.
The invention described by Nikitin [19] overcomes some of the limitations of the prior art by introducing a new family of filters (referred to as ‘SPART’, and, in particular, ‘FrankenSPART’ filters) which behave nonlinearly only during the occurrence of relatively high power disturbances, and maintain linear behavior otherwise. When an interference contains an impulsive component, SPART filters have the ability to improve the signal-to-noise ratio even if the spectral density of the noise lies entirely within the passband of the signal. They also do so without the traditional limitations of “clamping”-type limiters, such as slow recovery from saturation, phase reversal, and generation of excessive harmonics.
A FrankenSPART filter obtains the time derivative of the output as the difference between the input signal and a feedback of the output signal, then produces the output by comprising the following steps: (i) applying a comparator to confine said derivative to a certain range, (ii) linearly transforming the output of the comparator to introduce the slew rate and quantile parameters, and (iii) integrating said linearly transformed output of the comparator.
There are several significant limitations of the SPART filter family based on the FrankenSPART filtering method. These limitations relate to their implementations, configurability, performance, and applicability.
The implementations of the SPART filters rely on the use of comparators, since applying a comparator function is a required step in the SPART filtering method. A required step of applying a comparator complicates the topology and configurability of the SPART filters. Comparators (including clamping amplifiers) also suffer from a number of limitations that preclude their use in precision circuits, specifically large offsets, overdrive requirements, and response time limits. Practical implementation of comparators for the SPART filters may be complicated and expensive, as their range needs to be well defined and controlled, and this range is coupled to the subsequent linear transformation of the comparator output. In addition, the comparator functions are not defined for complex-valued and multidimensional vector signals by Nikitin [19], which limits the applicability of the SPART filters in complex-valued and multidimensional signal processing.
The required linear transformation step of the SPART filters is necessary for its configurability. While implementing a gain (and a level shift) is a relatively simple task, in the SPART filters the gain of the linear transformation stage is coupled with the range of the comparator. Most filtering tasks may require that the time parameter of a SPART filter remains constant, while its slew rate parameter is adjusted. In order to maintain a constant time parameter, both the gain of the linear transformation stage and the range of the comparator in a SPART filter need to be simultaneously and proportionally changed. This complicates the topology and configurability of the SPART filters and limits their dynamic range.
While the required explicit integration step in the SPART filters is a well-known task, constructing an explicit integrator introduces a limiting complication in the design and implementation. As a total, the need for the three explicit stages in a SPART filter increases the complexity, noise, and the component count of the circuit, while limiting its frequency performance (as a consequence of additional delays and frequency limitations of the stages) and its dynamic range.
In its linear regime, a FrankenSPART filter is identical to an RC integrator, that is, to a 1st order lowpass filter, where the time constant of the latter is equal to the time parameter of the FrankenSPART. A 1st order filter does not provide a selective frequency response needed for many applications. Thus, for example, in order to use a FrankenSPART filter in a communication channel, its time parameter needs to be sufficiently small so it does not significantly affect the baseband signal (see, for example, Nikitin [24]). A small time parameter degrades both the FrankenSPART circuit performance and its ability to effectively mitigate the impulsive noise.
When the interference affecting the signal of interest is impulsive, the prior art typically views this as a problem presenting an additional challenge rather then an opportunity to increase the overall effectiveness of the mitigation of the interference. Thus the prior art does not offer interference reduction methods that intentionally increase the impulsiveness of the interference in order to increase the effectiveness of its mitigation. This constitutes yet another common limitation of the typical prior art methods outlined in this section.